Itzhak Lior - Distributed Acoustic Sensing for Subsurface Imaging and Earthquake Early Warning

Itzhak Lior (Hebrew University of Jerusalem) Distributed Acoustic Sensing for Subsurface Imaging and Earthquake Early Warning Distributed acoustic sensing (DAS) is an ideal tool for recording both earthquakes and ambient noise owing to its dense spatial measurements and the ability to continuously record in harsh environments, such as underwater, close to the hypocenters of most of the largest earthquakes on Earth. In this talk, two DAS applications are presented: ambient noise tomography and earthquake early warning (EEW). Surface-wave dispersion-based ambient noise tomography is limited at small spatial scales by the underlying premise of negligible lateral variations. To image small-scale structures, this method should be augmented with objective and independent approaches that are not scale-limited. We show that power spectral densities (PSD) and auto-correlations (AC) of DAS data contain crucial information on lateral and vertical wave propagation. These methods are used to image a complex underwater basin using ambient noise recorded on a fiber deployed offshore Greece. A two-dimensional shear-wave velocity model was derived by analyzing Scholte-wave dispersion. PSD and AC reveal significant lateral variations across the short 2.5 km long fiber segment, including basin edge effects and scattered waves. These were used to further constrain and modify the velocity model. The modified model is supported by waveform simulations that qualitatively reproduce PSD and AC observations. Harnessing DAS for EEW using optical fibers deployed near on-land and underwater faults will allow for early detection and significantly improve warning times. We present a framework for real-time magnitude estimation and ground shaking prediction. Since currently, DAS earthquake datasets are limited to low-to-medium magnitudes, an empirical magnitude estimation approach is not feasible. Magnitude is estimated using an Omega-squared-model based theoretical description for acceleration root-mean-squares, and peak ground motions are predicted via ground motion prediction equation that are derived using the same theoretical model. The method is validated using a composite dataset of earthquakes from different tectonic settings. Being theoretical, the presented approach is readily applicable to any DAS array in any seismic region.